Brockwell A E, Kass R E, Schwartz A B
A. Brockwell and R. Kass are with the Department of Statistics at Carnegie Mellon University. A. Schwartz is with the Department of Neurobiology at the University of Pittsburgh.
Proc IEEE Inst Electr Electron Eng. 2007 May;95(5):881-898. doi: 10.1109/JPROC.2007.894703.
Over the past few decades, developments in technology have significantly improved the ability to measure activity in the brain. This has spurred a great deal of research into brain function and its relation to external stimuli, and has important implications in medicine and other fields. As a result of improved understanding of brain function, it is now possible to build devices that provide direct interfaces between the brain and the external world. We describe some of the current understanding of function of the motor cortex region. We then discuss a typical likelihood-based state-space model and filtering based approach to address the problems associated with building a motor cortical-controlled cursor or robotic prosthetic device. As a variation on previous work using this approach, we introduce the idea of using Markov chain Monte Carlo methods for parameter estimation in this context. By doing this instead of performing maximum likelihood estimation, it is possible to expand the range of possible models that can be explored, at a cost in terms of computational load. We demonstrate results obtained applying this methodology to experimental data gathered from a monkey.
在过去几十年中，技术发展显著提高了测量大脑活动的能力。这激发了大量关于脑功能及其与外部刺激关系的研究，并在医学和其他领域具有重要意义。由于对脑功能的理解有所改善，现在有可能制造出在大脑与外部世界之间提供直接接口的设备。我们描述了目前对运动皮层区域功能的一些理解。然后，我们讨论一种典型的基于似然性的状态空间模型和基于滤波的方法，以解决与构建运动皮层控制的光标或机器人假肢设备相关的问题。作为使用此方法的先前工作的一种变体，我们引入了在这种情况下使用马尔可夫链蒙特卡罗方法进行参数估计的想法。通过这样做而不是执行最大似然估计，可以扩大可探索的可能模型的范围，但代价是计算量增加。我们展示了将这种方法应用于从猴子收集的实验数据所获得的结果。
